



'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class 				a__:
    lowercase__					=       PegasusConfig
    lowercase__					=       {}
    lowercase__					=       """gelu"""







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Any		,     __snake_case					:    Union[str, Any]=13		,     __snake_case					:    Optional[int]=7		,     __snake_case					:    Optional[Any]=True		,     __snake_case					:    Optional[Any]=False		,     __snake_case					:    str=99		,     __snake_case					:    Optional[Any]=32		,     __snake_case					:    str=2		,     __snake_case					:    Optional[Any]=4		,     __snake_case					:    Union[str, Any]=37		,     __snake_case					:    Tuple=0.1		,     __snake_case					:    Union[str, Any]=0.1		,     __snake_case					:    str=40		,     __snake_case					:    Optional[Any]=2		,     __snake_case					:    int=1		,     __snake_case					:    Optional[Any]=0		,     ):
      a		:   Union[str, Any]	  =	parent
      a		:   str	  =	batch_size
      a		:   Any	  =	seq_length
      a		:   Union[str, Any]	  =	is_training
      a		:   Optional[Any]	  =	use_labels
      a		:   int	  =	vocab_size
      a		:   str	  =	hidden_size
      a		:   int	  =	num_hidden_layers
      a		:   str	  =	num_attention_heads
      a		:   Optional[Any]	  =	intermediate_size

      a		:   Tuple	  =	hidden_dropout_prob
      a		:   Union[str, Any]	  =	attention_probs_dropout_prob
      a		:   List[Any]	  =	max_position_embeddings
      a		:   Optional[int]	  =	eos_token_id
      a		:   Any	  =	pad_token_id
      a		:   Any	  =	bos_token_id







    def      lowercase_			( self					:    Union[str, Any]      ):
      a		:   Tuple	  =	ids_tensor([self.batch_size, self.seq_length - 1]		,     self.vocab_size      )
      a		:   int	  =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )		,     1      )
      a		:   Union[str, Any]	  =	tf.concat([input_ids, eos_tensor]		,     axis=1      )

      a		:   Optional[int]	  =	ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

      a		:   int	  =	self.config_cls(
          vocab_size=self.vocab_size		,     d_model=self.hidden_size		,     encoder_layers=self.num_hidden_layers		,     decoder_layers=self.num_hidden_layers		,     encoder_attention_heads=self.num_attention_heads		,     decoder_attention_heads=self.num_attention_heads		,     encoder_ffn_dim=self.intermediate_size		,     decoder_ffn_dim=self.intermediate_size		,     dropout=self.hidden_dropout_prob		,     attention_dropout=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     eos_token_ids=[2]		,     bos_token_id=self.bos_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.pad_token_id		,     **self.config_updates		,     )
      a		:   str	  =	prepare_pegasus_inputs_dict(__snake_case		,     __snake_case		,     __snake_case      )
      return config, inputs_dict







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    Dict      ):
      a		:   List[str]	  =	TFPegasusModel(config=__snake_case      ).get_decoder()
      a		:   Optional[Any]	  =	inputs_dict['input_ids']

      a		:   str	  =	input_ids[:1, :]
      a		:   List[Any]	  =	inputs_dict['attention_mask'][:1, :]
      a		:   List[Any]	  =	inputs_dict['head_mask']
      a		:   Optional[int]	  =	1

      # first forward pass
      a		:   Optional[int]	  =	model(__snake_case		,     attention_mask=__snake_case		,     head_mask=__snake_case		,     use_cache=__snake_case      )

      a  ,      a		:   Union[str, Any]	  =	outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      a		:   Optional[int]	  =	ids_tensor((self.batch_size, 3)		,     config.vocab_size      )
      a		:   Optional[Any]	  =	tf.cast(ids_tensor((self.batch_size, 3)		,     2      )		,     tf.inta      )

      # append to next input_ids and
      a		:   Union[str, Any]	  =	tf.concat([input_ids, next_tokens]		,     axis=-1      )
      a		:   str	  =	tf.concat([attention_mask, next_attn_mask]		,     axis=-1      )

      a		:   str	  =	model(__snake_case		,     attention_mask=__snake_case      )[0]
      a		:   Optional[Any]	  =	model(__snake_case		,     attention_mask=__snake_case		,     past_key_values=__snake_case      )[0]

      self.parent.assertEqual(next_tokens.shape[1]		,     output_from_past.shape[1]      )

      # select random slice
      a		:   Union[str, Any]	  =	int(ids_tensor((1,)		,     output_from_past.shape[-1]      )      )
      a		:   int	  =	output_from_no_past[:, -3:, random_slice_idx]
      a		:   Optional[int]	  =	output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(__snake_case		,     __snake_case		,     rtol=1e-3      )
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A=None					,     _A=None					,     _A=None					,     _A=None					,     _A=None					,     ):
  if attention_mask is None:
    a		:   Optional[Any]	  =	tf.cast(tf.math.not_equal(_A					,     config.pad_token_id							)					,     tf.inta							)
  if decoder_attention_mask is None:
    a		:   str	  =	tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape					,     dtype=tf.inta							),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,     config.pad_token_id							)					,     tf.inta							),
        ]					,     axis=-1					,     )
  if head_mask is None:
    a		:   Dict	  =	tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
  if decoder_head_mask is None:
    a		:   Dict	  =	tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
  if cross_attn_head_mask is None:
    a		:   int	  =	tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }

@require_tf
class 				a__(   lowerCamelCase__						,					lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    lowercase__					=       (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    lowercase__					=       (
        {
            """conversational""": TFPegasusForConditionalGeneration,
            """feature-extraction""": TFPegasusModel,
            """summarization""": TFPegasusForConditionalGeneration,
            """text2text-generation""": TFPegasusForConditionalGeneration,
            """translation""": TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowercase__					=       True
    lowercase__					=       False
    lowercase__					=       False







    def      lowercase_			( self					:    Union[str, Any]      ):
      a		:   Union[str, Any]	  =	TFPegasusModelTester(self      )
      a		:   List[str]	  =	ConfigTester(self		,     config_class=__snake_case      )







    def      lowercase_			( self					:    Any      ):
      self.config_tester.run_common_tests()







    def      lowercase_			( self					:    str      ):
      a		:   Tuple	  =	self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__snake_case      )

@require_sentencepiece
@require_tokenizers
@require_tf
class 				a__(   unittest.TestCase      ):
    lowercase__					=       [
        """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
        """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
    ]
    lowercase__					=       [
        """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
        """ reduce the risk of wildfires.""",
        """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    lowercase__					=       """google/pegasus-xsum"""







    @cached_property
    def      lowercase_			( self					:    str      ):
      return AutoTokenizer.from_pretrained(self.model_name      )







    @cached_property
    def      lowercase_			( self					:    int      ):
      a		:   Optional[Any]	  =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
      return model







    def      lowercase_			( self					:    List[str]		,     **__snake_case					:    Tuple      ):
      a		:   Union[str, Any]	  =	self.translate_src_text(**__snake_case      )
      assert self.expected_text == generated_words







    def      lowercase_			( self					:    Dict		,     **__snake_case					:    Optional[Any]      ):
      a		:   str	  =	self.tokenizer(self.src_text		,     **__snake_case		,     padding=__snake_case		,     return_tensors='tf'      )
      a		:   Dict	  =	self.model.generate(
          model_inputs.input_ids		,     attention_mask=model_inputs.attention_mask		,     num_beams=2		,     use_cache=__snake_case		,     )
      a		:   Any	  =	self.tokenizer.batch_decode(generated_ids.numpy()		,     skip_special_tokens=__snake_case      )
      return generated_words







    @slow
    def      lowercase_			( self					:    List[str]      ):
      self._assert_generated_batch_equal_expected()




'''simple docstring'''






from __future__ import annotations

lowerCAmelCase:   str					=		'Muhammad Umer Farooq'
lowerCAmelCase:   List[str]					=		'MIT'
lowerCAmelCase:   Tuple					=		'1.0.0'
lowerCAmelCase:   List[Any]					=		'Muhammad Umer Farooq'
lowerCAmelCase:   Optional[Any]					=		'contact@muhammadumerfarooq.me'
lowerCAmelCase:   Dict					=		'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    str      ):
      super().__init__()
      a		:   list[str]	  =	[]
      a		:   List[Any]	  =	domain







    def      lowercase_			( self					:    Dict		,     __snake_case					:    str		,     __snake_case					:    list[tuple[str, str | None]]      ):
      # Only parse the 'anchor' tag.
      if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
          # If href is defined, and not empty nor # print it.
          if name == "href" and value != "#" and value != "":
            # If not already in urls.
            if value not in self.urls:
              a		:   Tuple	  =	parse.urljoin(self.domain		,     __snake_case      )
              self.urls.append(__snake_case      )
def  lowerCamelCase__			(		_A							):
  return ".".join(get_sub_domain_name(_A							).split('.'							)[-2:]							)
def  lowerCamelCase__			(		_A							):
  return parse.urlparse(_A							).netloc
def  lowerCamelCase__			(		_A = "https://github.com"							):
  a		:   Any	  =	get_domain_name(_A							)

  # Initialize the parser
  a		:   Tuple	  =	Parser(_A							)

  try:
    # Open URL
    a		:   List[Any]	  =	requests.get(_A							)

    # pass the raw HTML to the parser to get links
    parser.feed(r.text							)

    # Get links and loop through
    a		:   Union[str, Any]	  =	set()
    for link in parser.urls:
      # open URL.
      # read = requests.get(link)
      try:
        a		:   int	  =	requests.get(_A							)
        # Get the valid email.
        a		:   Optional[Any]	  =	re.findall('[a-zA-Z0-9]+@' + domain					,     read.text							)
        # If not in list then append it.
        for email in emails:
          valid_emails.add(_A							)
      except ValueError:
        pass
  except ValueError:
    raise SystemExit(1							)

  # Finally return a sorted list of email addresses with no duplicates.
  return sorted(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Any					=		emails_from_url('https://github.com')
     print(F"{len(emails)} emails found:")
     print('\n'.join(sorted(emails)))